Blog Post:Today’s marketers have more tools available to them than ever before. But even with the recent boom in available solutions, it can be difficult to truly understand which marketing activities are driving the most value for your brand. The proliferation of multiple connected devices per user is making it harder to connect the dots from an attribution standpoint. The reality is that the proliferation of online devices has caused once trustable metrics to be misleading—specifically simple metrics that are learned on the first day of any marketer’s career, like reach and frequency, for example. Marketing attribution models allow you to see which campaigns and assets are the most successful, but what type of attribution is more important: cross-device attribution or cross-channel attribution?Without a doubt, both types of attribution are extremely important. Cross-device attribution is a necessary part of cross-channel attribution for one core reason—understanding that multiple touch points are actually the same person enriches your data. More accurately, it gives you a clearer picture of your marketing data. With these two tracking mechanisms so closely intertwined, how do you prioritize them?Cross-Channel Attribution vs. Cross-Device Attribution: What’s the Difference?Perhaps it is best to begin by truly understanding the differences between these two topics. Cross-channel attribution yields understanding of which banner ads, search results, and email campaigns are the most effective, assuming that users could have been exposed to touch points across all of them. Cross-device attribution is an opportunity to drill down to understand the experiences of each individual user across devices. Both start with cookie-level data, but cross-device attribution is about enhancing the cookie-level data to reveal that a single user is actually represented across multiple cookies. Cross-device attribution is a necessary part of cross-channel attribution for one core reason—understanding that multiple touch points are actually the same person enriches your data. More accurately, it gives you a clearer picture of your marketing data. Inherently, cross-device attribution is a subset of cross-channel attribution. Once you understand how certain campaigns are performing, it is natural to want to understand how many times an individual was touched by each campaign or even by multiple campaigns. To understand that level of detail, you must be able to track a user’s behavior across desktop, mobile, and tablet activity.The Rise of Cross-Device AttributionIn the past, only one true delivery platform existed: the desktop computer. So, when your campaign had a reach of 100 users, you could assume that 100—or nearly 100—unique users had been exposed. That is simply not the case in today’s world. In the past decade alone, we have seen the number of smartphones and tablets increase exponentially. Now, users may see the same message multiple times on multiple devices across many different marketing channels. In turn, this may prompt them to visit a company’s website or make a purchase on a different device from the one in which they viewed the campaign. In the grand scheme of things, this level of complexity (and opportunity) did not exist until recently.This result is a disconnect between our once-relevant industry terminology and the accuracy of what those terms mean today. A user with two devices can appear to be two unique users in our datasets. As marketers, we need to shift our focus to thinking about people and devices when we evaluate our data. Eventually, we will see the term “unique users” as a misleading and antiquated term. Achieving cross-channel attribution with consideration to cross-device data is predicated on understanding the metrics of “users” and “devices.” They are no longer synonymous.The Future of AttributionCurrently, marketers are trying to gain a complete picture of how customers reach their buying decisions. To do this, they need to use cross-channel and cross-device attribution in concert. In fact, they need to take it one step further and also use a process called “data stitching.” Data stitching can be a time-consuming process and also directionally accurate if the probabilistic method is used by itself. Since most reporting tools traditionally use a cookie to track user behavior, and many mobile devices do not support cookies, marketers run into quite a few problems with this process. Brands are still struggling to gain a full understanding of this new type of reporting.That is why the rise of the data-management platform (DMP) is so intriguing. Once brands can more effectively gather more first-party deterministic cross-device data and compile it in a DMP, the better they will be able to execute their marketing activities with more intelligence and relevant targeting aided by building device graphs. In turn, this allows them to build personalized campaigns that use a system of sequential messaging to move customers through the sales funnel, for example. E-commerce sites are starting to get a handle on this already by using unique user-generated IDs to identify users and track activity across devices. We can expect this marketing technique to continue to mature over the next few years and be picked up by other industries as well.Deterministically being able to message to a single user, knowingly, across two devices is an extremely rich targeting benefit, which also has the potential to be a better experience for the end user, not just the marketer. And flagging the data in that way is of the highest value. Understanding that a user’s journey to purchase included three digital channels is a huge step forward. But understanding that a user’s journey to purchase included media touch points across five different connected devices on three different channels before he eventually purchased with a desktop is a giant step forward to enhance our attribution. Attribution works best when the most granular level in your data is rich. Understanding that Person 123 has devices A, B, C, and D is paramount.Ultimately, both cross-channel and cross-device reporting are crucial to your data-driven marketing strategy. While at a macro level, cross-channel data has a number of implications that can be turned into actionable items now, being prepared to drill down into cross-device data will allow you to better attribute each channel’s contribution to your business and target your customers in ways that render insightful, not misleading data in your attribution. Once again, we see a plethora of opportunity on the marketing front that brands will not be able to take action on if their data is not in order.
Author:Matt Scharf
Date Created:February 3, 2016
Date Published:February 3, 2016
Headline:Which is More Important: Cross-Channel or Cross-Device Attribution?
Social Counts:
Keywords: #attribution #cross channel #Cross-Device Attribution #data
Publisher:Adobe
Image:https://blogs.adobe.com/digitalmarketing/wp-content/uploads/2016/02/AdobeStock_89829889-e1454432325394.jpeg

Which is More Important: Cross-Channel or Cross-Device Attribution?

Today’s marketers have more tools available to them than ever before. But even with the recent boom in available solutions, it can be difficult to truly understand which marketing activities are driving the most value for your brand. The proliferation of multiple connected devices per user is making it harder to connect the dots from an attribution standpoint. The reality is that the proliferation of online devices has caused once trustable metrics to be misleading—specifically simple metrics that are learned on the first day of any marketer’s career, like reach and frequency, for example. Marketing attribution models allow you to see which campaigns and assets are the most successful, but what type of attribution is more important: cross-device attribution or cross-channel attribution?

Without a doubt, both types of attribution are extremely important. Cross-device attribution is a necessary part of cross-channel attribution for one core reason—understanding that multiple touch points are actually the same person enriches your data. More accurately, it gives you a clearer picture of your marketing data. With these two tracking mechanisms so closely intertwined, how do you prioritize them?

Perhaps it is best to begin by truly understanding the differences between these two topics. Cross-channel attribution yields understanding of which banner ads, search results, and email campaigns are the most effective, assuming that users could have been exposed to touch points across all of them. Cross-device attribution is an opportunity to drill down to understand the experiences of each individual user across devices. Both start with cookie-level data, but cross-device attribution is about enhancing the cookie-level data to reveal that a single user is actually represented across multiple cookies. Cross-device attribution is a necessary part of cross-channel attribution for one core reason—understanding that multiple touch points are actually the same person enriches your data. More accurately, it gives you a clearer picture of your marketing data.

Inherently, cross-device attribution is a subset of cross-channel attribution. Once you understand how certain campaigns are performing, it is natural to want to understand how many times an individual was touched by each campaign or even by multiple campaigns. To understand that level of detail, you must be able to track a user’s behavior across desktop, mobile, and tablet activity.

The Rise of Cross-Device Attribution

In the past, only one true delivery platform existed: the desktop computer. So, when your campaign had a reach of 100 users, you could assume that 100—or nearly 100—unique users had been exposed. That is simply not the case in today’s world. In the past decade alone, we have seen the number of smartphones and tablets increase exponentially. Now, users may see the same message multiple times on multiple devices across many different marketing channels. In turn, this may prompt them to visit a company’s website or make a purchase on a different device from the one in which they viewed the campaign. In the grand scheme of things, this level of complexity (and opportunity) did not exist until recently.

This result is a disconnect between our once-relevant industry terminology and the accuracy of what those terms mean today. A user with two devices can appear to be two unique users in our datasets. As marketers, we need to shift our focus to thinking about people and devices when we evaluate our data. Eventually, we will see the term “unique users” as a misleading and antiquated term. Achieving cross-channel attribution with consideration to cross-device data is predicated on understanding the metrics of “users” and “devices.” They are no longer synonymous.

The Future of Attribution

Currently, marketers are trying to gain a complete picture of how customers reach their buying decisions. To do this, they need to use cross-channel and cross-device attribution in concert. In fact, they need to take it one step further and also use a process called “data stitching.” Data stitching can be a time-consuming process and also directionally accurate if the probabilistic method is used by itself. Since most reporting tools traditionally use a cookie to track user behavior, and many mobile devices do not support cookies, marketers run into quite a few problems with this process. Brands are still struggling to gain a full understanding of this new type of reporting.

That is why the rise of the data-management platform (DMP) is so intriguing. Once brands can more effectively gather more first-party deterministic cross-device data and compile it in a DMP, the better they will be able to execute their marketing activities with more intelligence and relevant targeting aided by building device graphs. In turn, this allows them to build personalized campaigns that use a system of sequential messaging to move customers through the sales funnel, for example. E-commerce sites are starting to get a handle on this already by using unique user-generated IDs to identify users and track activity across devices. We can expect this marketing technique to continue to mature over the next few years and be picked up by other industries as well.

Deterministically being able to message to a single user, knowingly, across two devices is an extremely rich targeting benefit, which also has the potential to be a better experience for the end user, not just the marketer. And flagging the data in that way is of the highest value. Understanding that a user’s journey to purchase included three digital channels is a huge step forward. But understanding that a user’s journey to purchase included media touch points across five different connected devices on three different channels before he eventually purchased with a desktop is a giant step forward to enhance our attribution. Attribution works best when the most granular level in your data is rich. Understanding that Person 123 has devices A, B, C, and D is paramount.

Ultimately, both cross-channel and cross-device reporting are crucial to your data-driven marketing strategy. While at a macro level, cross-channel data has a number of implications that can be turned into actionable items now, being prepared to drill down into cross-device data will allow you to better attribute each channel’s contribution to your business and target your customers in ways that render insightful, not misleading data in your attribution. Once again, we see a plethora of opportunity on the marketing front that brands will not be able to take action on if their data is not in order.

Matt Scharf

As Senior Manager of Display Media Operations & Analytics at Adobe Systems, Matt is also a customer of the Adobe Marketing Cloud. Matt leverages Audience Manager, Media Optimizer, Adobe Data Workbench, Target, and Adobe Analytics to support Adobe’s customer acquisition marketing programs. He focuses on technology enablement, audience segmentation, analytics, and attribution. Scharf is an outspoken member of the marketing community and a thought leader on the topics of attribution and viewability and a recipient of the ANA Genius Award in 2015 for Pioneering Analytics.